4 citations · 4 across the 4 of their papers we have counts for
6 papers
Active learning for medical code assignment
Martha Dais Ferreira, Michal Malyska, Nicola Sahar +3
Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-m…
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki +13
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major is…
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee +6
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data pr…
Scaling structural learning with NO-BEARS to infer causal transcriptome networks
Hao-Chih Lee, Matteo Danieletto, Riccardo Miotto +2
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimati…
Enhancing high-content imaging for studying microtubule networks at large-scale
Hao-Chih Lee, Sarah T Cherng, Riccardo Miotto +1
Given the crucial role of microtubules for cell survival, many researchers have found success using microtubule-targeting agents in the search for effective cancer therapeutics. Un…
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T Dudley +3
Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified…